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mrglvq from group de.cit-ec.ml (version 0.1.0)

This project contains a Java implementation of median relational generalized learning vector quantization as proposed by Nebel, Hammer, Frohberg, and Villmann (2015, doi:10.1016/j.neucom.2014.12.096). Given a matrix of pairwise distances D and a vector of labels Y it identifies prototypical data points (i.e. rows of D) which help to classify the data set using a simple nearest neighbor rule. In particular, the algorithm optimizes the generalized learning vector quantization cost function (Sato and Yamada, 1995) via an expectation maximization scheme where in each iteration one prototype 'jumps' to another data point in order to improve the cost function. If the cost function can not be improved anymore for any of the data points, the algorithm terminates.

Group: de.cit-ec.ml Artifact: mrglvq
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Artifact mrglvq
Group de.cit-ec.ml
Version 0.1.0
Last update 27. January 2018
Organization not specified
URL https://gitlab.ub.uni-bielefeld.de/bpaassen/median_relational_glvq
License The GNU General Public License, Version 3
Dependencies amount 1
Dependencies rng,
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